Publication

Effects of noise in different approaches for the statistics of polariton condensates

Davide Sarchi
2008
Journal Articles

Abstract

The second-order correlation function of the polariton condensate is calculated within two different approaches. Both approaches qualitatively reproduce the deviations from the full coherence of the second-order correlation function that have been observed in the experiments. However, a quantitative difference in the magnitude of the predicted deviations is found. This difference originates in the modeling of the polariton dynamics in the two approaches.

Official source

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Davide Sarchi
2008
Journal Articles

Abstract

Official source

Related concepts (13)

Root-mean-square deviation

The root-mean-square deviation (RMSD) or root-mean-square error (RMSE) is a frequently used measure of the differences between values (sample or population values) predicted by a model or an estimator and the values observed. The RMSD represents the square root of the second sample moment of the differences between predicted values and observed values or the quadratic mean of these differences. These deviations are called residuals when the calculations are performed over the data sample that was used for estimation and are called errors (or prediction errors) when computed out-of-sample.

Least absolute deviations

Least absolute deviations (LAD), also known as least absolute errors (LAE), least absolute residuals (LAR), or least absolute values (LAV), is a statistical optimality criterion and a statistical optimization technique based on minimizing the sum of absolute deviations (also sum of absolute residuals or sum of absolute errors) or the L1 norm of such values. It is analogous to the least squares technique, except that it is based on absolute values instead of squared values.

Mean signed deviation

In statistics, the mean signed difference (MSD), also known as mean signed deviation and mean signed error, is a sample statistic that summarises how well a set of estimates match the quantities that they are supposed to estimate. It is one of a number of statistics that can be used to assess an estimation procedure, and it would often be used in conjunction with a sample version of the mean square error.

Related publications (30)

Effects of noise in different approaches for the statistics of polariton condensates

Chat with Graph Search

Partial discharge localization in power transformer tanks using machine learning methods

Multisensing wearables for real-time monitoring of sweat electrolyte biomarkers during exercise and analysis on their correlation with core body temperature

The two-point correlation function covariance with fewer mocks